A review and comparative study on probabilistic object detection in autonomous driving
Capturing uncertainty in object detection is indispensable for safe autonomous driving. In
recent years, deep learning has become the de-facto approach for object detection, and …
recent years, deep learning has become the de-facto approach for object detection, and …
Efficient deep reinforcement learning with imitative expert priors for autonomous driving
Deep reinforcement learning (DRL) is a promising way to achieve human-like autonomous
driving. However, the low sample efficiency and difficulty of designing reward functions for …
driving. However, the low sample efficiency and difficulty of designing reward functions for …
Mind the gap! A study on the transferability of virtual versus physical-world testing of autonomous driving systems
Safe deployment of self-driving cars (SDC) necessitates thorough simulated and in-field
testing. Most testing techniques consider virtualized SDCs within a simulation environment …
testing. Most testing techniques consider virtualized SDCs within a simulation environment …
Uncertainties in onboard algorithms for autonomous vehicles: Challenges, mitigation, and perspectives
K Yang, X Tang, J Li, H Wang, G Zhong… - IEEE Transactions …, 2023 - ieeexplore.ieee.org
Autonomous driving is considered one of the revolutionary technologies sha** humanity's
future mobility and quality of life. However, safety remains a critical hurdle in the way of …
future mobility and quality of life. However, safety remains a critical hurdle in the way of …
When to trust AI: advances and challenges for certification of neural networks
Artificial intelligence (AI) has been advancing at a fast pace and it is now poised for
deployment in a wide range of applications, such as autonomous systems, medical …
deployment in a wide range of applications, such as autonomous systems, medical …
Thirdeye: Attention maps for safe autonomous driving systems
Automated online recognition of unexpected conditions is an indispensable component of
autonomous vehicles to ensure safety even in unknown and uncertain situations. In this …
autonomous vehicles to ensure safety even in unknown and uncertain situations. In this …
Monte Carlo dropout for uncertainty estimation and motor imagery classification
D Milanés-Hermosilla, R Trujillo Codorniú… - Sensors, 2021 - mdpi.com
Motor Imagery (MI)-based Brain–Computer Interfaces (BCIs) have been widely used as an
alternative communication channel to patients with severe motor disabilities, achieving high …
alternative communication channel to patients with severe motor disabilities, achieving high …
Robustness of bayesian neural networks to gradient-based attacks
Vulnerability to adversarial attacks is one of the principal hurdles to the adoption of deep
learning in safety-critical applications. Despite significant efforts, both practical and …
learning in safety-critical applications. Despite significant efforts, both practical and …
Uncertainty evaluation of object detection algorithms for autonomous vehicles
The safety of the intended functionality (SOTIF) has become one of the hottest topics in the
field of autonomous driving. However, no testing and evaluating system for SOTIF …
field of autonomous driving. However, no testing and evaluating system for SOTIF …
Uncertainty quantification via a memristor Bayesian deep neural network for risk-sensitive reinforcement learning
Many advanced artificial intelligence tasks, such as policy optimization, decision making and
autonomous navigation, demand high-bandwidth data transfer and probabilistic computing …
autonomous navigation, demand high-bandwidth data transfer and probabilistic computing …